The Eureka Clinical AI platform, developed by ConcertAI’s TeraRecon, now incorporates InferRead Lung CT.AI from Infervision to offer clinicians advanced artificial intelligence capabilities for analyzing lung CT scans, detecting abnormalities, and accurately identifying and labeling various types of nodules.
Moreover, the addition of InferRead Lung CT.AI to the Eureka Clinical AI platform facilitates integration within the existing medical workflow and delivers quantitative results to the patient’s entire care team within seconds to support radiologists, surgeons, and oncologists in making better, more informed decisions for patient care.
Lung cancer screening programs have been designed to encourage early diagnosis and treatment of high-risk population meeting certain criteria. The screening process involves low-dose CT (LDCT) scans to determine any presence of lung nodules or early-stage lung disease. Small nodules, however, can be very difficult to detect and missed diagnoses are not uncommon.
“With the InferRead Lung CT.AI algorithm on Eureka Clinical AI, we’ll be able to help clinicians by supporting automated reading to aid radiologists in pulmonary nodule detection in lung CT scans, increasing accuracy and efficiency,” says Dan McSweeney, president of TeraRecon.
“The tremendous potential for lung cancer screening to reduce mortality in the U.S. is very much unrealized due to a combination of reasons,” adds Eliot Siegel, MD., professor and vice chair of research information systems in radiology at the University of Maryland School of Medicine.
Siegel continues, “Based on our experience reviewing the algorithm for the past several months and my observations of its extensive use and testing internationally, I believe that Infervision’s InferRead Lung CT.AI application can serve as a robust lung nodule ‘spell-checker’ with the potential to improve diagnostic accuracy, reduce reading times, and integrate with the image review workflow.”
